Nuxt3 version of Triggerscore frontend

AI Recommender System - Recommends you similar movies based on Directors, Tags, Name, Type, Actors, Genre etc

Site que indica filmes com base em suas escolhas

This repository offers a movie recommendation system that uses Neural Collaborative Filtering and Non-negative Matrix Factorization (NNMF) for generating user-item matrices, combined with Cuckoo-Search and K-means clustering for optimization. The algorithm leverages collaborative filtering to make fast movie suggestions based on single preferences

HarvardX Data Science Professional Certificate - Machine Learning Project

SilverScreenAnalytics is a data analysis project that explores a movie dataset to uncover patterns and trends in the film industry. It analyzes variables such as budget, revenue, popularity, and vote averages, with insights on the most expensive and profitable movies, genre distribution, and relationships between key factors.

A Python-based movie data analyzer that explores the top 10 most popular movies from an IMDb dataset. It provides various visualizations, including correlations between budget and revenue, genre distributions, and more, with detailed statistics to gain insights into movie trends.

This project implements a content-based filtering model for recommending movies. The model uses various features extracted from a dataset of the top 1000 movies from IMDb to compute similarities and recommend similar movies.

Page about film's review using the TMDB API to get the information (in progress)